Presence, the sense of being in a virtual environment (VE), is analysed in an embodied cognition framework. We propose that VEs are mentally represented as meshed sets of patterns of actions and that presence is experienced when these actions include the perceived possibility to navigate and move the own body in the VE. A factor analyses of survey data shows 3 different presence components: spatial presence, involvement, and judgement of realness. A path analysis shows that spatial presence is mostly determined by sources of meshed patterns of actions: interaction with the VE, understanding of dynamics, and perception of dramatic meaning. 1 The role of psychology in virtual environments When we use virtual environments (VEs), we often experience presence, the subjective sense of being in the virtual place. Presence is observable when people interact in and with a virtual world as if they were there, when they grasp for virtual objects or develop fear of virtual cliffs [1]. T...

The computer metaphor for the mind or brain has long outlived its usefulness, being based on Cartesian ideas. Connectionism has not broken free from this metaphor, and this has stunted the directions connectionist research has taken. The subordinate role of timing in computations has resulted in networks with real-value timelags on signals passing between nodes being ignored. The notion of representation in connectionism is generally confused; this can be clarified when at all times it is made explicit who or what Q and S are in the formula "P is used by Q to represent R to S". Frequently they may be layers or modules within a network, but the typical confusion is symptomatic of the computer metaphor which in practice favours feedforward and militates against arbitrarily connected networks. Rejecting this metaphor, an alternative paradigm is suggested of a brain as a complex dynamical system; investigating the dynamics of arbitrarily connected networks with real-valued timelags, speci...

It is generally thought that skilled behavior in human beings results from a functional hierarchy of the motor control system, within which reusable motor primitives are flexibly integrated into various sensori-motor sequence patterns. The underlying neural mechanisms governing the way in which continuous sensori-motor flows are segmented into primitives and the way in which series of primitives are integrated into various behavior sequences have, however, not yet been clarified. In earlier studies, this functional hierarchy has been realized through the use of explicit hierarchical structure, with local modules representing motor primitives in the lower level and a higher module representing sequences of primitives switched via additional mechanisms such as gate-selecting. When sequences contain similarities and overlap, however, a conflict arises in such earlier models between generalization and segmentation, induced by this separated modular structure. To address this issue, we propose a different type of neural network model. The current model neither makes use of separate local modules to represent primitives nor introduces explicit hierarchical structure. Rather than forcing architectural hierarchy onto the system, functional hierarchy emerges through a form of self-organization that is based on two distinct types of neurons, each with different time properties (‘‘multiple timescales’’). Through the introduction of multiple timescales, continuous sequences of behavior are segmented into reusable primitives, and the primitives, in turn, are flexibly integrated into novel sequences. In experiments, the proposed network model, coordinating the physical body of a humanoid robot through

Background noise is the irregular variation across repeated measurements of human performance. Background noise remains after task and treatment effects are minimized. Background noise refers to intrinsic sources of variability, the intrinsic dynamics of mind and body, and the internal workings of a living being. Two experiments demonstrate 1/f scaling (pink noise) in simple reaction times and speeded word naming times, which round out a catalog of laboratory task demonstrations that background noise is pink noise. Ubiquitous pink noise suggests processes of mind and body that change each other’s dynamics. Such interaction-dominant dynamics are found in systems that self-organize their behavior. Self-organization provides an unconventional perspective on cognition, but this perspective closely parallels a contemporary interdisciplinary view of living systems. Psychological science usually ignores the background noise in behavioral data. Background noise is what is left over when task demands, experimental manipulations, and other external sources of variability have been eliminated or minimized. What we call background noise is treated as random variability in most research, the nuisance factor in factorial experiments. We argue, to the

Recently there have been a number of proposals for the use of artificial evolution as a radically new approach to the development of control systems for autonomous robots. This paper explains the artificial evolution approach, using work at Sussex to illustrate it. The paper revolves around a case study on the concurrent evolution of control networks and visual sensor morphologies for a mobile robot. Wider intellectual evolutionary simulations as a new potentially useful tool in theoretical biology.

One can study the the evolution of sensorimotor functionality by synthesizing this process in an abstract arti cial life model, speci cally, a population of agents that interact with each other and with their environment in a way that allows natural selection implicitly to shape their sensorimotor couplings. The present paper de nes very general measures of environmental and sensory uncertainty, and of action's direct and indirect e ectsonperception, and reports a series of observations of these quantities in the context of the model.

In this paper, we propose an unsupervised neural network allowing a robot to learn sensory-motor associations with a delayed reward. The robot task is to learn the "meaning" of pictograms in order to "survive" in a maze. First, we introduce a new neural conditioning rule (PCR: Probabilistic Conditioning Rule) allowing to test hypotheses (associations between visual categories and movements) during a given time span. Afterwards, we describe a real maze experiment with our mobile robot. We propose a neural architecture overcoming the difficulty to build visual categories dynamically while associating them to movements. Third, we propose to use our algorithm on a simulation in order to test it exhaustively. We give the results for different kinds of mazes. Finally, we conclude by showing the limitations of approaches that do not take into account the intrinsic complexity of a reasoning based on image recognition. Keywords: Neural Networks, Unsupervised Learning, Topological Maps...

Maturana and Varela's notion of autopoiesis has the potential to transform the conceptual foundation of biology, as well as the cognitive, behavioral and brain sciences. In order to fully realize this potential, however, the concept of autopoiesis and its many consequences require significant further theoretical and empirical development. A crucial step in this direction is the formulation and analysis of models of autopoietic systems. This paper sketches the beginnings of such a project by examining a glider from the Game of Life in autopoietic terms. Such analyses can clarify some of the key ideas underlying autopoiesis and draw attention to some of the central open issues. This paper also examines the relationship between an autopoietic perspective on cognition and recent work on dynamical approaches to the behavior and cognition of situated, embodied agents.

A framework for understanding and exploiting embodiment is presented which is not dependent on any specific ontological context. This framework is founded on a new definition of embodiment, based on the relational dynamics that exist between biological organisms and their environments, and inspired by the structural dynamics of the Escherichia coli bacteria. Recognition is given to the role played by physically instantiated bodies, but in such a way that this can be meaningfully abstracted within the constraints implied by the term 'embodiment', and applied in a variety of operational contexts. This is supported by ongoing experimental work in which the relational dynamics that exist between E. coli and its environment are applied in a variety of software environments, using Cellular Automata (CA) with artificial 'sensory' and 'effector' surfaces, producing qualitatively similar 'chemotaxic' behaviours in a variety of operational domains.

Abstract In this paper we describe a form of communication that could be used for lifelong learning as contribution to cultural computing. We call it Kansei Mediation. It is a multimedia communication concept that can cope with nonverbal, emotional and Kansei information. We introduce the distinction between the concepts of Kansei Communication and Kansei Media. We then develop a theory of communication (i.e. Kansei Mediation) as a combination of both. Based on recent results from brain research the proposed concept of Kansei Mediation is developed and discussed. The biased preference towards consciousness in established communication theories is critically reviewed and the relationship to pre- and unconscious brain processes explored. There are two tenets of the Kansei Mediation communication theory: (1) communication based on connected unconciousness, and (2) Satori as the ultimate form of experience.